Scale at Speed™
Smarter AI at the Edge: The Next Wave of Autonomous Enterprise Computing

How the convergence of Agentic AI and Smart Edge is reshaping decision-making in real-time
In today's hyper-connected digital landscape, data is generated at the edge, in locations such as factories, vehicles, cities, stores, and homes, rather than in centralized locations. In addition to this, enterprises are embracing a powerful convergence of Smart AI and Smart Edge technologies to harness this distributed data for real-time decisions.
This fusion brings intelligence to the point of data creation (source), enabling real-time decisions, higher resilience, and more adaptive operations. The next evolution is already underway: Agentic AI, event-driven intelligence, real-time orchestration, and autonomous systems that react, decide, and act independently at the edge.
Why Edge Computing? Why Now?
The traditional cloud model struggles to meet the demands of low-latency, high-volume, privacy-sensitive use cases. Edge computing solves this by moving computing and intelligence closer to where data is generated.
Challenges Addressed by Smart Edge Technologies:
- Latency: Enables millisecond-level responses for mission-critical use cases.
- Privacy & Compliance: Processes sensitive data locally, aligning with regulations.
- Bandwidth Efficiency: Reduces cloud traffic through local filtering and inference.
- Resilience: Ensures operations continue uninterrupted, even in the event of poor connectivity.
Smart Edge Meets Smart AI and Agentic Intelligence
What truly makes today's edge intelligent is the rise of Agentic AI-autonomous, context-aware agents that respond to data events in real time.
These intelligent AI agents analyze data, sense, decide, and act independently or collaboratively. Located at the edge, these agents react to events such as machine anomalies, traffic spikes, or security breaches, orchestrating immediate actions across various systems.
Key Enablers Include:
- Event-Driven Intelligence: Systems detect and respond to real-world events as they happen.
- Agentic AI: Autonomous agents operate with goals, context, and adaptability.
- Real-Time Orchestration: Dynamic workflows across edge, cloud, and hybrid environments.
These capabilities can be integrated into a modular edge ecosystem that leverages GenAI, 5G, and event-driven computing to automate decisions in real time and orchestrate complex enterprise processes with minimal or no human intervention.
Industry Momentum: Accelerating the Shift to Edge
- 75% of enterprise data will be generated and processed outside centralized data centers by the end of 2025. *
- Over 40% of large enterprises are adopting edge strategies*.
- Global edge computing spending is projected to reach $378 billion by 2028*.
This momentum is driven by the need for real-time responsiveness, intelligent automation,, and cost-effective scalability—all of which are delivered by the Smart AI and Smart Edge paradigm.
Real-World Use Cases:
Manufacturing
Predictive maintenance with event-driven agents:
- AI detects early signs of equipment failure.
- The agent then triggers the repair workflow, reassigns tasks, and updates digital twin models.
Retail
Edge intelligence for in-store optimization:
- Vision analytics detects empty shelves or high footfall.
- The agent initiates shelf restocking or redirects staff based on the level of foot traffic.
Logistics
Autonomous fleet orchestration:
- Edge sensors detect route delays or environmental changes.
- AI agent re-routes deliveries in real-time and automatically notifies customers, minimizing delays and improving customer satisfaction.
Healthcare
Edge AI for remote diagnostics:
- Patient vital signs trigger anomaly detection.
- The AI agent initiates a teleconsultation or emergency protocol based on predefined thresholds.
What's Next: From Automation to Autonomy
As the edge platforms evolve, enterprises are shifting from automation to autonomy. The edge is no longer a passive infrastructure but an active layer of intelligent agents orchestrating actions across physical and digital worlds.
Enterprises that adopt this model can:
- Make decisions at the moment data is generated.
- Reduce downtime and operational risk.
- Create self-optimizing systems that scale with demand.
Act Now, Lead Tomorrow
Smart AI and Edge technologies are no longer futuristic ideas. They're becoming essential for staying competitive. With Agentic AI, event-driven systems, and real-time orchestration, businesses can transition from reacting to events to anticipating and responding to them autonomously.
The edge is emerging as a critical decision-making layer across various industries, including manufacturing, logistics, healthcare, and retail. In this place, intelligent actions occur instantly, rather than simply serving as a data channel. Organizations that embrace this shift early will gain faster insights, greater agility, and systems that continually optimize themselves.

Vipul Rattan leads Multi-Tower Offerings and Strategic Growth for Large Deals at Tech Mahindra, driving integrated solutions and business value across industries and verticals.More
Vipul Rattan leads Multi-Tower Offerings and Strategic Growth for Large Deals at Tech Mahindra, driving integrated solutions and business value across industries and verticals. With over 22 years of experience in IT and telecom, he has successfully led global sales, GTM strategy, offering development, digital transformation, and sales enablement initiatives across Tech Mahindra, Tata Communications, Gilead, and Oracle, shaping high-value portfolios and steering international expansion. Vipul is also instrumental in shaping Tech Mahindra’s Global Capability Center (GCC) offering, spearheading the design and execution of GCC services globally. His leadership has positioned Tech Mahindra as a trusted partner for enterprises seeking to establish and scale their global operations through strategic GCC models.
Less